Despite extensive research in this area and the fact that academic researchers achieve high classification accuracy in laboratory conditions, there is still a gap between academic research in sEMG pattern recognition and commercialized solutions [1]}. In this context, one of the objectives for reducing the gap is to focus on the development of DNN-based models that not only have high recognition accuracy but also have minimal processing complexity, allowing them to be embedded in low-power devices such as wearable controllers [2]}, [3]}. Furthermore, the designed DNN-based models should be based on the minimum number of electrodes while estimating the desired gestures within an acceptable delay time [1]}, [5]}. Consequently, in this paper, we develop the novel Hierarchical Depth-wise Convolution along with the Attention Mechanism (\(\text{HDCAM}\) ) model for HGR based on sparse sEMG signals to fill this gap by meeting criteria such as improving the accuracy and reducing the number of parameters. The \(\text{HDCAM}\)  is developed based on the \(\text{Ninapro}\)  [6]}, [7]} database, which is one of the most well-known sparse multi-channel sEMG benchmark datasets.
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